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Stata's expertise lies in the analysis of time based data. Stata provides not only the basic time series models like ARIMA but even the multivariate equivalents (VAR/VEC-Models) as well. Further you are able to model volatility using GARCH-models in Stata. Kaplan-Meier-curves are the way to analyse survival times, while mixed models help to analyse panel data. A mighty scripting language completes the package.
Stata produces all kinds of classical statistics. You can use it for descriptive statistics, hypothesis testing and visualization of data. Typically Stata is used in research and development. The large amount of different statistical methods helps scientists in all fields of applications (Social science, econometrics, epidimiology, medical research).
No matter if you are a student or a senior researcher, there is always the right version of Stata available: Stata/IC, Stata/SE and Stata/MP
Arguments for Stata:
- Used in research and development
- Wide range of statistical and graphical methods
- Comprehensive statistical software
- Flexible and especially powerful for analysis of time series
- Easy to learn but mighty scripting language
Stata statistical software is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package.
Easy to learn yet fully programmable for the most demanding data management and statistical requirements.
With Stata's menus and dialogs, you can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. You can completely reshape your data, create group-level variables for panel or longitudinal data, graph a receiver operating characteristics (ROC) curve or impulse-response function (IRF), perform a case-control analysis, estimate a random-effects count-data model or a Cox proportional hazards model, or compute marginal effects from a nonlinear estimator. You can even access the dialog boxes for each command directly from the online help system. T his is a great way to explore all of the capabilities of Stata.
Stata Software is available in 3 different flavors
Whether you’re a student or a seasoned research professional, we have a package designed to suit your needs:
- Stata/MP: The fastest version of Stata (for quad-core, dual-core, and multicore/multiprocessor computers) that can analyze the most data
- Stata/SE: Stata for large datasets
- Stata/IC: Stata for mid-sized datasets
- Numerics by Stata: Stata for embedded and web applications
Stata/MP is the fastest and largest version of Stata. Virtually any current computer can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel i3, i5, i7, i9, Xeon, and Celeron, and AMD multi-core chips. On dual-core chips, Stata/MP runs 40% faster overall and 72% faster where it matters, on the time-consuming estimation commands. With more than two cores or processors, Stata/MP is even faster. Find out more about Stata/MP.
Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can purchase a Stata/MP license for up to the number of cores on your machine (maximum is 64). For example, if your machine has eight cores, you can purchase a Stata/MP license for eight cores, four cores, or two cores.
Stata/MP can also analyze more data than any other flavor of Stata. Stata/MP can analyze 10 to 20 billion observations given the current largest computers, and is ready to analyze up to 1 trillion observations once computer hardware catches up.
Stata/SE and Stata/IC differ only in the dataset size that each can analyze. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyze up to 2 billion observations.
Stata/IC allows datasets with as many as 2,048 variables and 2 billion observations. Stata/IC can have at most 798 independent variables in a model.
Numerics by Stata can support any of the data sizes listed above in an embedded environment.
All the above flavors have the same complete set of features and include PDF documentation.
|Maximum number of variables||2,048||32,767||120|
|Maximum number of observations||2.14 billion||2.14 billion||Up to 20 billion|
|Maximum number of independent variables||798||10,998||10,998|
|Multicore support (Time to run logistic regression with 5 million obs and 10 covariates )||1-core/ 10.0 sec||1-core/ 10.0 sec||2- core (5.0 sec), 4-core (2,6 sec), 4+ core (even faster)|
|Complete suite of statistical features||Yes!||Yes!||Yes!|
|Matrix programming language||Yes!||Yes!||Yes!|
|Complete PDF documentation||Yes!||Yes!||Yes!|
|Exceptional technical support||Yes!||Yes!||Yes!|
|Includes within-release updates||Yes!||Yes!||Yes!|
|64-bit version available||Yes!||Yes!||Yes!|
|Windows, macOS, and Linux||Yes!||Yes!||Yes!|
|Memory requirements||1 GB||2 GB||4 GB|
|Disk space requirements||1 GB||1 GB||1 GB|
* The maximum number of observations is limited only by the amount of available RAM on your system.
Stata scripting language
Stata's scripting language is easy to learn and helps you to get the most out of your data. It allows not only to use and modify the existing routines to generate standard reports, but can easily be extended with newly created statistical functions.
Efficient Datamanagent with Stata
Datamanagement with Stata is easy and efficient. Joining datasets, creating new variables or producing summary tables is done in no time.
Professional Graphics with Stata
Stata provides professional graphics that can directly be used for documents and publications. This includes not only pre-defined standard graphs but although highly customizeable graphics.
Trialversion of Stata
The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customer-service/evaluate-stata/
Package Memory Disk space
|Package Memory Disk space
Stata/MP 4 GB 1 GB
Stata/SE 2 GB 1 GB
Stata/IC 1 GB 1 GB
Package Memory Disk space
Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)
||Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer||
Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out
Stata for Windows®
• Windows 10 *
• Windows 8 *
• Windows 7 *
• Windows Vista *
• Windows Server 2016, 2012, 2008, 2003 *
* 64-bit and 32-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD Find out if your OS is 64-bit compliant.
Stata for Mac®
• Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer Find out if your OS is 64-bit compliant.
Stata for Linux
• Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux
• For xstata, you need to have GTK 2.24 installed
Disk space Stata/MP 4 GB 1 GB Stata/SE 2 GB 1 GB Stata/IC 1 GB 1 GB
Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)
Type bayes: in front of any of 45 Stata estimation commands to fit a Bayesian regression model.
Write your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts.
Because sometimes where you are matters.
Fit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more.
When ... your science ... says ... your model ... is ... nonlinear in its parameters
Do you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for?
We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility.
When you know something matters.
But have no idea how.
Small number of groups?
Consider Bayesian multilevel modeling.
Your time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does.
Stata has long had estimators for random effects (random intercepts) in panel data.
The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.
Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.
Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include
Power analysis for comparing
when you randomize clusters instead of individuals
Counts are common. How many:
Fish did you catch?
Patents does a firm generate?
Outcomes are not always seen.
Folks evade the game warden.
Accidents are not always reported.
Some firms prefer trade secrets to patents.
So you need Poisson models with sample selection.
More in panel dataNonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients
More in graphicsTransparency in graphs SVG export
More in statisticsBayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection models And yet more
More in the interfaceStata in Swedish Stata in Chinese Improvements to the Do-file Editor
And, even more
The whole feature list you will find under the following link:
data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results
summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables
regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS
Multilevel mixed-effects models
generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models
Binary, count, and discrete outcomes
logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects
Longitudinal data/panel data
random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances
Generalized linear models (GLMs)
ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals
Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping
exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables
balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts
factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses
hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses
Resampling and simulation methods
bootstrapping, jackknife and Monte Carlo simulation; permutation tests
Tests, predictions, and effects
Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests
line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots
multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression
Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis
standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM
ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation
nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions
Simple maximum likelihood
specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators
Programmable maximum likelihood
user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives
Other statistical methods
kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality
adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins
interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming
ability to install new commands, web updating, web file sharing, latest Stata news
Section 508 compliance, accessibility for persons with disabilities
A sample session of Stata for Mac, Unix, or Windows.
User-written commands for meta-analysis, data management, survival, econometrics
Graphical user interface
menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets
line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots
20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references
Power and sample size
power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs
inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)
SEM (Structural equation modeling)
graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data
statistical; random-number; mathematical; string; date and time
Embedded statistical computations
Numerics by Stata
Contrasts, pairwise comparisons, and margins
compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots
GMM an nonlinear regression
generalized method of moments (GMM); nonlinear regression